Research on cultured meat has primarily focused on the mass proliferation or differentiation of muscle cells; thus, the food characteristics of cultured meat remain relatively underexplored. As the quality of meat is determined by its organoleptic properties, cultured meat with similar sensory characteristics to animal-derived meat is highly desirable. In this study, we control the organoleptic and nutritional properties of cultured meat by tailoring the 2D differentiation of primary bovine myoblasts and primary bovine adipose-derived mesenchymal stem cells on gelatin/alginate scaffolds with varying stiffness. We assess the effect of muscle and adipose differentiation quality on the sensory properties of cultured meat. Thereafter, we fabricate cultured meat with similar sensory profiles to that of conventional beef by assembling the muscle and adipose constructs composed of highly differentiated cells. We introduce a strategy to produce cultured meat with enriched food characteristics by regulating cell differentiation with scaffold engineering.

Significance: Deciphering molecular mechanisms at the glycome level in mammalian brain remains a missing piece of the puzzle in molecular neuroscience due to the intrinsic complexity of glycosylation and the lack of analytical tools. Here, we uncovered the variation and diversity of glycome expression in human and mouse brain samples according to spatial and temporal differences. We further constructed a comprehensive synthesis map using glycans structurally elucidated by LC-MS/MS and found strong evidence on the conservation and developmental divergence of human and mouse prefrontal cortex N-glycome. Our data could be the reference for future mammalian brain glycome study. Furthermore, it provides valuable information on human brain glycome, which has languished in relative obscurity.

Human glycoproteins exhibit enormous heterogeneity at each N-glycosite, but few studies have attempted to globally characterize the site-specific structural features. We have developed Integrated GlycoProteome Analyzer (I-GPA) including mapping system for complex N-glycoproteomes, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. Using an N-glycopeptide database that we constructed, we created novel scoring algorithms with decoy glycopeptides, where 95 N-glycopeptides from standard α1-acid glycoprotein were identified with 0% false positives, giving the same results as manual validation. Additionally automated label-free quantitation method was first developed that utilizes the combined intensity of top three isotope peaks at three highest MS spectral points. The efficiency of I-GPA was demonstrated by automatically identifying 619 site-specific N-glycopeptides with FDR ≤ 1%, and simultaneously quantifying 598 N-glycopeptides, from human plasma samples that are known to contain highly glycosylated proteins. Thus, I-GPA platform could make a major breakthrough in high-throughput mapping of complex N-glycoproteomes, which can be applied to biomarker discovery and ongoing global human proteome project.

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.